



'''simple docstring'''





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class 				lowerCAmelCase_						:
    __UpperCAmelCase				=			field(
        metadata={'help': 'The output directory where the model will be written.'}    ,     )
    __UpperCAmelCase				=			field(
        metadata={
            'help': (
                'The encoder model checkpoint for weights initialization.'
                'Don\'t set if you want to train an encoder model from scratch.'
            )
        }    ,     )
    __UpperCAmelCase				=			field(
        metadata={
            'help': (
                'The decoder model checkpoint for weights initialization.'
                'Don\'t set if you want to train a decoder model from scratch.'
            )
        }    ,     )
    __UpperCAmelCase				=			field(
        default=a_    ,     metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}		)
    __UpperCAmelCase				=			field(
        default=a_    ,     metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}		)




def 						_a       (    ):
   snake_case       :				Any		    =HfArgumentParser((ModelArguments,)				)
   (snake_case					)       :				List[str]		    =parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      snake_case       :				List[str]		    =AutoConfig.from_pretrained(model_args.encoder_config_name				)
   # Use pretrained encoder model's config
   else:
      snake_case       :				List[str]		    =AutoConfig.from_pretrained(model_args.encoder_model_name_or_path				)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      snake_case       :				Optional[int]		    =AutoConfig.from_pretrained(model_args.decoder_config_name				)
   # Use pretrained decoder model's config
   else:
      snake_case       :				Dict		    =AutoConfig.from_pretrained(model_args.decoder_model_name_or_path				)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   snake_case       :				List[str]		    =True
   snake_case       :				str		    =True

   snake_case       :				Any		    =FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path	,		decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path	,		encoder_config=a__	,		decoder_config=a__	,		)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   snake_case       :				Dict		    =decoder_config.decoder_start_token_id
   snake_case       :				Any		    =decoder_config.pad_token_id
   if decoder_start_token_id is None:
      snake_case       :				Optional[Any]		    =decoder_config.bos_token_id
   if pad_token_id is None:
      snake_case       :				int		    =decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   snake_case       :				Optional[int]		    =decoder_config.eos_token_id
   snake_case       :				int		    =decoder_start_token_id
   snake_case       :				Optional[int]		    =pad_token_id

   snake_case       :				Optional[Any]		    =AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

   snake_case       :				List[Any]		    =AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
   snake_case       :				int		    =tokenizer.convert_ids_to_tokens(model.config.pad_token_id				)

   model.save_pretrained(model_args.output_dir				)
   image_processor.save_pretrained(model_args.output_dir				)
   tokenizer.save_pretrained(model_args.output_dir				)


if __name__ == "__main__":
      main()








def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return x if y == 0 else greatest_common_divisor(a__  ,	x % y)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return (x * y) // greatest_common_divisor(a__  ,	a__)


def 					_UpperCAmelCase				(						a__ = 2_0):

			'''simple docstring'''



			a_						:						Any 							= 1
			for i in range(1  ,	n + 1):
						a_						:						Tuple 							= lcm(a__  ,	a__)
			return g


if __name__ == "__main__":
	print(F"""{solution() = }""")


